The exploding gradient problem occurs during training deep neural networks when the gradients of the loss function become excessively large. This often happens when the weights of the network are initialized with large values or when using certain activation functions. When gradients are too large, the model's weights can update by excessively large amounts, leading to instability during training.
This issue can result in NaN (Not a Number) values in the model’s weights, causing the training process to fail. To mitigate this problem, techniques such as gradient clipping, weight regularization, or using better weight initialization methods (like Xavier or He initialization) are employed.
Addressing the exploding gradient problem is particularly important in deep networks and recurrent neural networks (RNNs), where it can be more pronounced due to the depth or sequential nature of the model.